Curator's Take
This article tackles one of NISQ computing's biggest challenges by developing quantum annealing-inspired algorithms that work within the constraints of today's noisy, limited-qubit devices. The researchers demonstrate how approximate quantum annealing can serve as an intelligent starting point for QAOA, potentially overcoming the notorious barren plateau problem that often plagues variational quantum algorithms when initialized randomly. Their new evolving Hamiltonian quantum optimization approach is particularly clever, using intermediate steps to guide the optimization process rather than jumping directly to the final target Hamiltonian. These findings could significantly improve the practical performance of near-term quantum optimization algorithms, bridging the gap between the theoretical promise of quantum annealing and what's actually achievable on current hardware.
— Mark Eatherly
Summary
We study algorithms inspired by quantum annealing that are suited for the NISQ era. First, we analyze approximate quantum annealing (AQA), which employs a discretized annealing ansatz in which the time step and the number of layers are allowed to deviate from a faithful implementation of quantum annealing. Parameter scans identify regimes that reproduce annealing-like behavior with reduced resources, making them more suitable for NISQ devices. The resulting parameters can then be used as an effective warm start for the quantum approximate optimization algorithm (QAOA), improving its performance compared to random initializations. We also introduce evolving Hamiltonian quantum optimization (EHQO), a multistep variational scheme that guides the optimization process through intermediate Hamiltonians derived from the standard annealing Hamiltonian. Numerical simulations on sets of hard 2-SAT instances suggest that quantum annealing-inspired algorithms provide practical strategies for enhancing variational quantum optimization.